The goal of EdGwas is to help clustering outcome components (traits) that share some feature (genetic component) using polygenic risk scores (PRS).
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("abuchardt/EdGwas")
This is a basic example on simulated data:
library(EdGwas)
#> Registered S3 methods overwritten by 'ggplot2':
#> method from
#> [.quosures rlang
#> c.quosures rlang
#> print.quosures rlang
# Gaussian
N <- 100 #
q <- 9
p <- 500 #
set.seed(1)
X <- matrix(sample(0:2, N*p, replace=TRUE), nrow=N, ncol=p)
B <- matrix(0, nrow = p, ncol = q)
B[1:2, 1:5] <- 1
Y <- X %*% B + matrix(rnorm(N*q), nrow = N, ncol = q)
Run 5-fold cross-validation for edgwas
cvfit <- cv.edgwas(x = X, y = Y, nfolds = 5)
#> i: 1 , ....................
#> i: 2 , ....................
#> i: 3 , ....................
#> i: 4 , ....................
#> i: 5 , ....................
plot(cvfit, which = 1)
plot(cvfit, which = 3)